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Mental arithmetic task classification with convolutional neural network based on spectral-temporal features from EEG

Abstract : In recent years, neuroscientists have been interested to the development of brain-computer interface (BCI) devices. Patients with motor disorders may benefi t from BCIs as a means of communication and for the restoration of motor functions. Electroencephalography (EEG) is one of most used for evaluating the neuronal activity. In many computer vision applications, deep neural networks (DNN) show significant advantages. Towards to ultimate usage of DNN, we present here a shallow neural network that uses mainly two convolutional neural network (CNN) layers, with relatively few parameters and fast to learn spectral-temporal features from EEG. We compared this models to three other neural network models with different depths applied to a mental arithmetic task using eye-closed state adapted for patients suffering from motor disorders and a decline in visual functions. Experimental results showed that the shallow CNN model outperformed all the other models and achieved the highest classifi cation accuracy of 90.68%. It’s also more robust to deal with cross-subject classifi cation issues: only 3% standard deviation of accuracy instead of 15.6% from conventional method.
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https://hal.mines-ales.fr/hal-03711249
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Submitted on : Friday, July 1, 2022 - 11:28:55 AM
Last modification on : Friday, August 5, 2022 - 2:44:08 PM

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  • HAL Id : hal-03711249, version 1

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Zaineb Ajra, Binbin Xu, Gérard Dray, Jacky Montmain, S. Perrey. Mental arithmetic task classification with convolutional neural network based on spectral-temporal features from EEG. 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022, Jul 2022, Glasgow, United Kingdom. ⟨hal-03711249⟩

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